基于注意力的语音特征在发言者之间转移
Hangbok Lee1, Minjae Cho1, Hyuk-Yoon Kwon1
1Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.
Frontiers in artificial intelligence
|March 12, 2024
概括
这项研究引入了一种新的语音合成方法,以将源音箱的声音风格转移到目标音箱的语音. 该技术修改了语音合成模型中的注意力权重,以转移风格.
科学领域:
- 语音合成 语音合成
- 机器学习 机器学习
- 数字信号处理是数字信号处理.
背景情况:
- 语音合成中的扬声器特征对于自然性至关重要.
- 现有的方法可能会在准确的风格转移方面扎.
- 深度学习模型中的注意力机制为特征提取提供了潜力.
研究的目的:
- 开发一种有效的方法,将源音箱特征纳入目标音箱语音中.
- 为了使语音合成模型能够与源音箱风格生成目标音箱语音.
- 探索注意力模型在捕捉和传递说话者特定特征中的作用.
主要方法:
- 在语音合成框架内专注于注意力模型.
- 提取的扬声器特征包括光谱图,音调,强度,形式,脉冲和语音休息.
- 训练出源和目标扬声器的单独模型,然后将源注意力权重替换为目标权重.
主要成果:
- 成功生成了表现出源扬声器风格的目标扬声器语音.
- 使用相似性分析与五个评估指标验证模型有效性.
- 通过现实世界的例子展示了实际应用.
结论:
- 拟议的方法提供了一种简单而有效的方法,用于语音合成中的扬声器风格转移.
- 注意重量操纵是一种实现风格模仿的可行技术.
- 该模型对需要个性化或风格化语音生成的应用具有前景.
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